Skip to main content
This is a pre-production deployment of Warehouse. Changes made here affect the production instance of PyPI (
Help us improve Python packaging - Donate today!

A toolkit for phase-based feature detection

Project Description

This toolkit consists of a set of functions which use information contained within the phase of a Fourier-transformed image to detect localised features such as edges, blobs and corners. These methods have the key advantage that the properties they measure are invariant with respect to image brightness and contrast.

phasecong:Phase congruency using oriented filters
phasecongmono:Fast phase congruency using monogenic filters
phasesym:Phase symmetry using oriented filters
phasesymmono:Fast phase symmetry using monogenic filters

For more information on a particular function, see the associated docstring and the references therein.


$ python install

Fast(er) Fourier Transforms

All of the functions in this module make use of the Fast Fourier Transform (FFT), and their speed significantly depends on the module used to provide FFT functions. If it is available, the pyFFTW module will be used. This provides Python bindings to the FFTW C library, and is substantially faster than fftpack, the default for scipy.

To install pyFFTW:

$ pip install pyfftw


These functions were originally written for MATLAB by Peter Kovesi, and were ported to Python by Alistair Muldal. The original MATLAB code, as well as further explanatory information and references are available from Peter Kovesi’s website.

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

The software is provided “as is”, without warranty of any kind.

Release History

This version
History Node


History Node


History Node


Download Files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

File Name & Hash SHA256 Hash Help Version File Type Upload Date
(15.3 kB) Copy SHA256 Hash SHA256
Source Apr 20, 2016

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting